# coding=utf-8

# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.

# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

#     http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

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# See the License for the specific language governing permissions and

# limitations under the License.

""" PyTorch XLNet model.

"""

from __future__ import absolute_import, division, print_function, unicode_literals



import json

import logging

import math

import os

import sys

from io import open



import torch

from torch import nn

from torch.nn import functional as F

from torch.nn import CrossEntropyLoss, MSELoss



from .modeling_utils import PreTrainedModel, prune_linear_layer, SequenceSummary, PoolerAnswerClass, PoolerEndLogits, PoolerStartLogits

from .configuration_xlnet import XLNetConfig

from .file_utils import add_start_docstrings





logger = logging.getLogger(__name__)



XLNET_PRETRAINED_MODEL_ARCHIVE_MAP = {

    'xlnet-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-base-cased-pytorch_model.bin",

    'xlnet-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-large-cased-pytorch_model.bin",

}





def build_tf_xlnet_to_pytorch_map(model, config, tf_weights=None):

    """ A map of modules from TF to PyTorch.

        I use a map to keep the PyTorch model as

        identical to the original PyTorch model as possible.

    """



    tf_to_pt_map = {}



    if hasattr(model, 'transformer'):

        if hasattr(model, 'lm_loss'):

            # We will load also the output bias

            tf_to_pt_map['model/lm_loss/bias'] = model.lm_loss.bias

        if hasattr(model, 'sequence_summary') and 'model/sequnece_summary/summary/kernel' in tf_weights:

            # We will load also the sequence summary

            tf_to_pt_map['model/sequnece_summary/summary/kernel'] = model.sequence_summary.summary.weight

            tf_to_pt_map['model/sequnece_summary/summary/bias'] = model.sequence_summary.summary.bias

        if hasattr(model, 'logits_proj') and config.finetuning_task is not None \
                and 'model/regression_{}/logit/kernel'.format(config.finetuning_task) in tf_weights:

            tf_to_pt_map['model/regression_{}/logit/kernel'.format(config.finetuning_task)] = model.logits_proj.weight

            tf_to_pt_map['model/regression_{}/logit/bias'.format(config.finetuning_task)] = model.logits_proj.bias



        # Now load the rest of the transformer

        model = model.transformer



    # Embeddings and output

    tf_to_pt_map.update({'model/transformer/word_embedding/lookup_table': model.word_embedding.weight,

                         'model/transformer/mask_emb/mask_emb': model.mask_emb})



    # Transformer blocks

    for i, b in enumerate(model.layer):

        layer_str = "model/transformer/layer_%d/" % i

        tf_to_pt_map.update({

            layer_str + "rel_attn/LayerNorm/gamma": b.rel_attn.layer_norm.weight,

            layer_str + "rel_attn/LayerNorm/beta": b.rel_attn.layer_norm.bias,

            layer_str + "rel_attn/o/kernel": b.rel_attn.o,

            layer_str + "rel_attn/q/kernel": b.rel_attn.q,

            layer_str + "rel_attn/k/kernel": b.rel_attn.k,

            layer_str + "rel_attn/r/kernel": b.rel_attn.r,

            layer_str + "rel_attn/v/kernel": b.rel_attn.v,

            layer_str + "ff/LayerNorm/gamma": b.ff.layer_norm.weight,

            layer_str + "ff/LayerNorm/beta": b.ff.layer_norm.bias,

            layer_str + "ff/layer_1/kernel": b.ff.layer_1.weight,

            layer_str + "ff/layer_1/bias": b.ff.layer_1.bias,

            layer_str + "ff/layer_2/kernel": b.ff.layer_2.weight,

            layer_str + "ff/layer_2/bias": b.ff.layer_2.bias,

        })



    # Relative positioning biases

    if config.untie_r:

        r_r_list = []

        r_w_list = []

        r_s_list = []

        seg_embed_list = []

        for b in model.layer:

            r_r_list.append(b.rel_attn.r_r_bias)

            r_w_list.append(b.rel_attn.r_w_bias)

            r_s_list.append(b.rel_attn.r_s_bias)

            seg_embed_list.append(b.rel_attn.seg_embed)

    else:

        r_r_list = [model.r_r_bias]

        r_w_list = [model.r_w_bias]

        r_s_list = [model.r_s_bias]

        seg_embed_list = [model.seg_embed]

    tf_to_pt_map.update({

        'model/transformer/r_r_bias': r_r_list,

        'model/transformer/r_w_bias': r_w_list,

        'model/transformer/r_s_bias': r_s_list,

        'model/transformer/seg_embed': seg_embed_list})

    return tf_to_pt_map



def load_tf_weights_in_xlnet(model, config, tf_path):

    """ Load tf checkpoints in a pytorch model

    """

    try:

        import numpy as np

        import tensorflow as tf

    except ImportError:

        logger.error("Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "

            "https://www.tensorflow.org/install/ for installation instructions.")

        raise

    # Load weights from TF model

    init_vars = tf.train.list_variables(tf_path)

    tf_weights = {}

    for name, shape in init_vars:

        logger.info("Loading TF weight {} with shape {}".format(name, shape))

        array = tf.train.load_variable(tf_path, name)

        tf_weights[name] = array



    # Build TF to PyTorch weights loading map

    tf_to_pt_map = build_tf_xlnet_to_pytorch_map(model, config, tf_weights)



    for name, pointer in tf_to_pt_map.items():

        logger.info("Importing {}".format(name))

        if name not in tf_weights:

            logger.info("{} not in tf pre-trained weights, skipping".format(name))

            continue

        array = tf_weights[name]

        # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v

        # which are not required for using pretrained model

        if 'kernel' in name and ('ff' in name or 'summary' in name or 'logit' in name):

            logger.info("Transposing")

            array = np.transpose(array)

        if isinstance(pointer, list):

            # Here we will split the TF weigths

            assert len(pointer) == array.shape[0]

            for i, p_i in enumerate(pointer):

                arr_i = array[i, ...]

                try:

                    assert p_i.shape == arr_i.shape

                except AssertionError as e:

                    e.args += (p_i.shape, arr_i.shape)

                    raise

                logger.info("Initialize PyTorch weight {} for layer {}".format(name, i))

                p_i.data = torch.from_numpy(arr_i)

        else:

            try:

                assert pointer.shape == array.shape

            except AssertionError as e:

                e.args += (pointer.shape, array.shape)

                raise

            logger.info("Initialize PyTorch weight {}".format(name))

            pointer.data = torch.from_numpy(array)

        tf_weights.pop(name, None)

        tf_weights.pop(name + '/Adam', None)

        tf_weights.pop(name + '/Adam_1', None)



    logger.info("Weights not copied to PyTorch model: {}".format(', '.join(tf_weights.keys())))

    return model





def gelu(x):

    """ Implementation of the gelu activation function.

        XLNet is using OpenAI GPT's gelu (not exactly the same as BERT)

        Also see https://arxiv.org/abs/1606.08415

    """

    cdf = 0.5 * (1.0 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))

    return x * cdf





def swish(x):

    return x * torch.sigmoid(x)





ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish}





XLNetLayerNorm = nn.LayerNorm





class XLNetRelativeAttention(nn.Module):

    def __init__(self, config):

        super(XLNetRelativeAttention, self).__init__()

        self.output_attentions = config.output_attentions



        if config.d_model % config.n_head != 0:

            raise ValueError(

                "The hidden size (%d) is not a multiple of the number of attention "

                "heads (%d)" % (config.d_model, config.n_head))



        self.n_head = config.n_head

        self.d_head = config.d_head

        self.d_model = config.d_model

        self.scale = 1 / (config.d_head ** 0.5)



        self.q = nn.Parameter(torch.FloatTensor(config.d_model, self.n_head, self.d_head))

        self.k = nn.Parameter(torch.FloatTensor(config.d_model, self.n_head, self.d_head))

        self.v = nn.Parameter(torch.FloatTensor(config.d_model, self.n_head, self.d_head))

        self.o = nn.Parameter(torch.FloatTensor(config.d_model, self.n_head, self.d_head))

        self.r = nn.Parameter(torch.FloatTensor(config.d_model, self.n_head, self.d_head))



        self.r_r_bias = nn.Parameter(torch.FloatTensor(self.n_head, self.d_head))

        self.r_s_bias = nn.Parameter(torch.FloatTensor(self.n_head, self.d_head))

        self.r_w_bias = nn.Parameter(torch.FloatTensor(self.n_head, self.d_head))

        self.seg_embed = nn.Parameter(torch.FloatTensor(2, self.n_head, self.d_head))



        self.layer_norm = XLNetLayerNorm(config.d_model, eps=config.layer_norm_eps)

        self.dropout = nn.Dropout(config.dropout)



    def prune_heads(self, heads):

        raise NotImplementedError



    @staticmethod

    def rel_shift(x, klen=-1):

        """perform relative shift to form the relative attention score."""

        x_size = x.shape



        x = x.reshape(x_size[1], x_size[0], x_size[2], x_size[3])

        x = x[1:, ...]

        x = x.reshape(x_size[0], x_size[1] - 1, x_size[2], x_size[3])

        # x = x[:, 0:klen, :, :]

        x = torch.index_select(x, 1, torch.arange(klen, device=x.device, dtype=torch.long))



        return x



    @staticmethod

    def rel_shift_bnij(x, klen=-1):

        x_size = x.shape



        x = x.reshape(x_size[0], x_size[1], x_size[3], x_size[2])

        x = x[:, :, 1:, :]

        x = x.reshape(x_size[0], x_size[1], x_size[2], x_size[3]-1)

        # Note: the tensor-slice form was faster in my testing than torch.index_select

        #       However, tracing doesn't like the nature of the slice, and if klen changes

        #       during the run then it'll fail, whereas index_select will be fine.

        x = torch.index_select(x, 3, torch.arange(klen, device=x.device, dtype=torch.long))

        # x = x[:, :, :, :klen]



        return x



    def rel_attn_core(self, q_head, k_head_h, v_head_h, k_head_r, seg_mat=None, attn_mask=None, head_mask=None):

        """Core relative positional attention operations."""



        # content based attention score

        ac = torch.einsum('ibnd,jbnd->bnij', q_head + self.r_w_bias, k_head_h)



        # position based attention score

        bd = torch.einsum('ibnd,jbnd->bnij', q_head + self.r_r_bias, k_head_r)

        bd = self.rel_shift_bnij(bd, klen=ac.shape[3])



        # segment based attention score

        if seg_mat is None:

            ef = 0

        else:

            ef = torch.einsum('ibnd,snd->ibns', q_head + self.r_s_bias, self.seg_embed)

            ef = torch.einsum('ijbs,ibns->bnij', seg_mat, ef)



        # merge attention scores and perform masking

        attn_score = (ac + bd + ef) * self.scale

        if attn_mask is not None:

            # attn_score = attn_score * (1 - attn_mask) - 1e30 * attn_mask

            if attn_mask.dtype == torch.float16:

                attn_score = attn_score - 65500 * torch.einsum('ijbn->bnij', attn_mask)

            else:

                attn_score = attn_score - 1e30 * torch.einsum('ijbn->bnij', attn_mask)



        # attention probability

        attn_prob = F.softmax(attn_score, dim=3)

        attn_prob = self.dropout(attn_prob)



        # Mask heads if we want to

        if head_mask is not None:

            attn_prob = attn_prob * torch.einsum('ijbn->bnij', head_mask)



        # attention output

        attn_vec = torch.einsum('bnij,jbnd->ibnd', attn_prob, v_head_h)



        if self.output_attentions:

            return attn_vec, torch.einsum('bnij->ijbn', attn_prob)



        return attn_vec



    def post_attention(self, h, attn_vec, residual=True):

        """Post-attention processing."""

        # post-attention projection (back to `d_model`)

        attn_out = torch.einsum('ibnd,hnd->ibh', attn_vec, self.o)



        attn_out = self.dropout(attn_out)

        if residual:

            attn_out = attn_out + h

        output = self.layer_norm(attn_out)



        return output



    def forward(self, h, g,

                      attn_mask_h, attn_mask_g,

                      r, seg_mat,

                      mems=None, target_mapping=None, head_mask=None):

        if g is not None:

            ###### Two-stream attention with relative positional encoding.

            # content based attention score

            if mems is not None and mems.dim() > 1:

                cat = torch.cat([mems, h], dim=0)

            else:

                cat = h



            # content-based key head

            k_head_h = torch.einsum('ibh,hnd->ibnd', cat, self.k)



            # content-based value head

            v_head_h = torch.einsum('ibh,hnd->ibnd', cat, self.v)



            # position-based key head

            k_head_r = torch.einsum('ibh,hnd->ibnd', r, self.r)



            ##### h-stream

            # content-stream query head

            q_head_h = torch.einsum('ibh,hnd->ibnd', h, self.q)



            # core attention ops

            attn_vec_h = self.rel_attn_core(

                q_head_h, k_head_h, v_head_h, k_head_r, seg_mat=seg_mat, attn_mask=attn_mask_h, head_mask=head_mask)



            if self.output_attentions:

                attn_vec_h, attn_prob_h = attn_vec_h



            # post processing

            output_h = self.post_attention(h, attn_vec_h)



            ##### g-stream

            # query-stream query head

            q_head_g = torch.einsum('ibh,hnd->ibnd', g, self.q)



            # core attention ops

            if target_mapping is not None:

                q_head_g = torch.einsum('mbnd,mlb->lbnd', q_head_g, target_mapping)

                attn_vec_g = self.rel_attn_core(

                    q_head_g, k_head_h, v_head_h, k_head_r, seg_mat=seg_mat, attn_mask=attn_mask_g, head_mask=head_mask)



                if self.output_attentions:

                    attn_vec_g, attn_prob_g = attn_vec_g



                attn_vec_g = torch.einsum('lbnd,mlb->mbnd', attn_vec_g, target_mapping)

            else:

                attn_vec_g = self.rel_attn_core(

                    q_head_g, k_head_h, v_head_h, k_head_r, seg_mat=seg_mat, attn_mask=attn_mask_g, head_mask=head_mask)



                if self.output_attentions:

                    attn_vec_g, attn_prob_g = attn_vec_g



            # post processing

            output_g = self.post_attention(g, attn_vec_g)



            if self.output_attentions:

                attn_prob = attn_prob_h, attn_prob_g



        else:

            ###### Multi-head attention with relative positional encoding

            if mems is not None and mems.dim() > 1:

                cat = torch.cat([mems, h], dim=0)

            else:

                cat = h



            # content heads

            q_head_h = torch.einsum('ibh,hnd->ibnd', h, self.q)

            k_head_h = torch.einsum('ibh,hnd->ibnd', cat, self.k)

            v_head_h = torch.einsum('ibh,hnd->ibnd', cat, self.v)



            # positional heads

            k_head_r = torch.einsum('ibh,hnd->ibnd', r, self.r)



            # core attention ops

            attn_vec = self.rel_attn_core(

                q_head_h, k_head_h, v_head_h, k_head_r, seg_mat=seg_mat, attn_mask=attn_mask_h, head_mask=head_mask)



            if self.output_attentions:

                attn_vec, attn_prob = attn_vec



            # post processing

            output_h = self.post_attention(h, attn_vec)

            output_g = None



        outputs = (output_h, output_g)

        if self.output_attentions:

            outputs = outputs + (attn_prob,)

        return outputs



class XLNetFeedForward(nn.Module):

    def __init__(self, config):

        super(XLNetFeedForward, self).__init__()

        self.layer_norm = XLNetLayerNorm(config.d_model, eps=config.layer_norm_eps)

        self.layer_1 = nn.Linear(config.d_model, config.d_inner)

        self.layer_2 = nn.Linear(config.d_inner, config.d_model)

        self.dropout = nn.Dropout(config.dropout)

        if isinstance(config.ff_activation, str) or \
                (sys.version_info[0] == 2 and isinstance(config.ff_activation, unicode)):

            self.activation_function = ACT2FN[config.ff_activation]

        else:

            self.activation_function = config.ff_activation



    def forward(self, inp):

        output = inp

        output = self.layer_1(output)

        output = self.activation_function(output)

        output = self.dropout(output)

        output = self.layer_2(output)

        output = self.dropout(output)

        output = self.layer_norm(output + inp)

        return output



class XLNetLayer(nn.Module):

    def __init__(self, config):

        super(XLNetLayer, self).__init__()

        self.rel_attn = XLNetRelativeAttention(config)

        self.ff = XLNetFeedForward(config)

        self.dropout = nn.Dropout(config.dropout)



    def forward(self, output_h, output_g,

                attn_mask_h, attn_mask_g,

                r, seg_mat, mems=None, target_mapping=None, head_mask=None):

        outputs = self.rel_attn(output_h, output_g, attn_mask_h, attn_mask_g,

                                r, seg_mat, mems=mems, target_mapping=target_mapping,

                                head_mask=head_mask)

        output_h, output_g = outputs[:2]



        if output_g is not None:

            output_g = self.ff(output_g)

        output_h = self.ff(output_h)



        outputs = (output_h, output_g) + outputs[2:]  # Add again attentions if there are there

        return outputs





class XLNetPreTrainedModel(PreTrainedModel):

    """ An abstract class to handle weights initialization and

        a simple interface for dowloading and loading pretrained models.

    """

    config_class = XLNetConfig

    pretrained_model_archive_map = XLNET_PRETRAINED_MODEL_ARCHIVE_MAP

    load_tf_weights = load_tf_weights_in_xlnet

    base_model_prefix = "transformer"



    def _init_weights(self, module):

        """ Initialize the weights.

        """

        if isinstance(module, (nn.Linear, nn.Embedding)):

            # Slightly different from the TF version which uses truncated_normal for initialization

            # cf https://github.com/pytorch/pytorch/pull/5617

            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)

            if isinstance(module, nn.Linear) and module.bias is not None:

                module.bias.data.zero_()

        elif isinstance(module, XLNetLayerNorm):

            module.bias.data.zero_()

            module.weight.data.fill_(1.0)

        elif isinstance(module, XLNetRelativeAttention):

            for param in [module.q, module.k, module.v, module.o, module.r,

                          module.r_r_bias, module.r_s_bias, module.r_w_bias,

                          module.seg_embed]:

                param.data.normal_(mean=0.0, std=self.config.initializer_range)

        elif isinstance(module, XLNetModel):

                module.mask_emb.data.normal_(mean=0.0, std=self.config.initializer_range)





XLNET_START_DOCSTRING = r"""    The XLNet model was proposed in

    `XLNet: Generalized Autoregressive Pretraining for Language Understanding`_

    by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.

    XLnet is an extension of the Transformer-XL model pre-trained using an autoregressive method

    to learn bidirectional contexts by maximizing the expected likelihood over all permutations

    of the input sequence factorization order.



    The specific attention pattern can be controlled at training and test time using the `perm_mask` input.



    Do to the difficulty of training a fully auto-regressive model over various factorization order,

    XLNet is pretrained using only a sub-set of the output tokens as target which are selected

    with the `target_mapping` input.



    To use XLNet for sequential decoding (i.e. not in fully bi-directional setting), use the `perm_mask` and

    `target_mapping` inputs to control the attention span and outputs (see examples in `examples/run_generation.py`)



    This model is a PyTorch `torch.nn.Module`_ sub-class. Use it as a regular PyTorch Module and

    refer to the PyTorch documentation for all matter related to general usage and behavior.



    .. _`XLNet: Generalized Autoregressive Pretraining for Language Understanding`:

        http://arxiv.org/abs/1906.08237



    .. _`torch.nn.Module`:

        https://pytorch.org/docs/stable/nn.html#module



    Parameters:

        config (:class:`~transformers.XLNetConfig`): Model configuration class with all the parameters of the model.

            Initializing with a config file does not load the weights associated with the model, only the configuration.

            Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.

"""



XLNET_INPUTS_DOCSTRING = r"""

    Inputs:

        **input_ids**: ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:

            Indices of input sequence tokens in the vocabulary.

            XLNet is a model with relative position embeddings so you can either pad the inputs on

            the right or on the left.

            Indices can be obtained using :class:`transformers.XLNetTokenizer`.

            See :func:`transformers.PreTrainedTokenizer.encode` and

            :func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.

        **token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:

            A parallel sequence of tokens (can be used to indicate various portions of the inputs).

            The type indices in XLNet are NOT selected in the vocabulary, they can be arbitrary numbers and

            the important thing is that they should be different for tokens which belong to different segments.

            The model will compute relative segment differences from the given type indices:

            0 if the segment id of two tokens are the same, 1 if not.

        **attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:

            Mask to avoid performing attention on padding token indices.

            Mask values selected in ``[0, 1]``:

            ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.

        **mems**: (`optional`)

            list of ``torch.FloatTensor`` (one for each layer):

            that contains pre-computed hidden-states (key and values in the attention blocks) as output by the model

            (see `mems` output below). Can be used to speed up sequential decoding and attend to longer context.

            To activate mems you need to set up config.mem_len to a positive value which will be the max number of tokens in

            the memory output by the model. E.g. `model = XLNetModel.from_pretrained('xlnet-base-case, mem_len=1024)` will

            instantiate a model which can use up to 1024 tokens of memory (in addition to the input it self).

        **perm_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, sequence_length)``:

            Mask to indicate the attention pattern for each input token with values selected in ``[0, 1]``:

            If ``perm_mask[k, i, j] = 0``, i attend to j in batch k;

            if ``perm_mask[k, i, j] = 1``, i does not attend to j in batch k.

            If None, each token attends to all the others (full bidirectional attention).

            Only used during pretraining (to define factorization order) or for sequential decoding (generation).

        **target_mapping**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, num_predict, sequence_length)``:

            Mask to indicate the output tokens to use.

            If ``target_mapping[k, i, j] = 1``, the i-th predict in batch k is on the j-th token.

            Only used during pretraining for partial prediction or for sequential decoding (generation).

        **token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:

            A parallel sequence of tokens (can be used to indicate various portions of the inputs).

            The type indices in XLNet are NOT selected in the vocabulary, they can be arbitrary numbers and

            the important thing is that they should be different for tokens which belong to different segments.

            The model will compute relative segment differences from the given type indices:

            0 if the segment id of two tokens are the same, 1 if not.

        **input_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:

            Mask to avoid performing attention on padding token indices.

            Negative of `attention_mask`, i.e. with 0 for real tokens and 1 for padding.

            Kept for compatibility with the original code base.

            You can only uses one of `input_mask` and `attention_mask`

            Mask values selected in ``[0, 1]``:

            ``1`` for tokens that are MASKED, ``0`` for tokens that are NOT MASKED.

        **head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:

            Mask to nullify selected heads of the self-attention modules.

            Mask values selected in ``[0, 1]``:

            ``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.

        **inputs_embeds**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, embedding_dim)``:

            Optionally, instead of passing ``input_ids`` you can choose to directly pass an embedded representation.

            This is useful if you want more control over how to convert `input_ids` indices into associated vectors

            than the model's internal embedding lookup matrix.

"""



@add_start_docstrings("The bare XLNet Model transformer outputting raw hidden-states without any specific head on top.",

                      XLNET_START_DOCSTRING, XLNET_INPUTS_DOCSTRING)

class XLNetModel(XLNetPreTrainedModel):

    r"""

    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:

        **last_hidden_state**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)``

            Sequence of hidden-states at the last layer of the model.

        **mems**: (`optional`, returned when ``config.mem_len > 0``)

            list of ``torch.FloatTensor`` (one for each layer):

            that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model

            if config.mem_len > 0 else tuple of None. Can be used to speed up sequential decoding and attend to longer context.

            See details in the docstring of the `mems` input above.

        **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)

            list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)

            of shape ``(batch_size, sequence_length, hidden_size)``:

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.

        **attentions**: (`optional`, returned when ``config.output_attentions=True``)

            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

            When ``target_mapping is not None``, the attentions outputs are a list of 2-tuple of ``torch.FloatTensor``.



    Examples::



        tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased')

        model = XLNetModel.from_pretrained('xlnet-large-cased')

        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1

        outputs = model(input_ids)

        last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple



    """

    def __init__(self, config):

        super(XLNetModel, self).__init__(config)

        self.output_attentions = config.output_attentions

        self.output_hidden_states = config.output_hidden_states

        self.output_past = config.output_past



        self.mem_len = config.mem_len

        self.reuse_len = config.reuse_len

        self.d_model = config.d_model

        self.same_length = config.same_length

        self.attn_type = config.attn_type

        self.bi_data = config.bi_data

        self.clamp_len = config.clamp_len

        self.n_layer = config.n_layer



        self.word_embedding = nn.Embedding(config.n_token, config.d_model)

        self.mask_emb = nn.Parameter(torch.FloatTensor(1, 1, config.d_model))

        self.layer = nn.ModuleList([XLNetLayer(config) for _ in range(config.n_layer)])

        self.dropout = nn.Dropout(config.dropout)



        self.init_weights()



    def get_input_embeddings(self):

        return self.word_embedding



    def set_input_embeddings(self, new_embeddings):

        self.word_embedding = new_embeddings



    def _prune_heads(self, heads_to_prune):

        raise NotImplementedError



    def create_mask(self, qlen, mlen):

        """

        Creates causal attention mask. Float mask where 1.0 indicates masked, 0.0 indicates not-masked.



        Args:

            qlen: TODO Lysandre didn't fill

            mlen: TODO Lysandre didn't fill



        ::



                  same_length=False:      same_length=True:

                  <mlen > <  qlen >       <mlen > <  qlen >

               ^ [0 0 0 0 0 1 1 1 1]     [0 0 0 0 0 1 1 1 1]

                 [0 0 0 0 0 0 1 1 1]     [1 0 0 0 0 0 1 1 1]

            qlen [0 0 0 0 0 0 0 1 1]     [1 1 0 0 0 0 0 1 1]

                 [0 0 0 0 0 0 0 0 1]     [1 1 1 0 0 0 0 0 1]

               v [0 0 0 0 0 0 0 0 0]     [1 1 1 1 0 0 0 0 0]



        """

        attn_mask = torch.ones([qlen, qlen])

        mask_up = torch.triu(attn_mask, diagonal=1)

        attn_mask_pad = torch.zeros([qlen, mlen])

        ret = torch.cat([attn_mask_pad, mask_up], dim=1)

        if self.same_length:

            mask_lo = torch.tril(attn_mask, diagonal=-1)

            ret = torch.cat([ret[:, :qlen] + mask_lo, ret[:, qlen:]], dim=1)



        ret = ret.to(next(self.parameters()))

        return ret



    def cache_mem(self, curr_out, prev_mem):

        """cache hidden states into memory."""

        if self.reuse_len is not None and self.reuse_len > 0:

            curr_out = curr_out[:self.reuse_len]



        if prev_mem is None:

            new_mem = curr_out[-self.mem_len:]

        else:

            new_mem = torch.cat([prev_mem, curr_out], dim=0)[-self.mem_len:]



        return new_mem.detach()



    @staticmethod

    def positional_embedding(pos_seq, inv_freq, bsz=None):

        sinusoid_inp = torch.einsum('i,d->id', pos_seq, inv_freq)

        pos_emb = torch.cat([torch.sin(sinusoid_inp), torch.cos(sinusoid_inp)], dim=-1)

        pos_emb = pos_emb[:, None, :]



        if bsz is not None:

            pos_emb = pos_emb.expand(-1, bsz, -1)



        return pos_emb



    def relative_positional_encoding(self, qlen, klen, bsz=None):

        """create relative positional encoding."""

        freq_seq = torch.arange(0, self.d_model, 2.0, dtype=torch.float)

        inv_freq = 1 / torch.pow(10000, (freq_seq / self.d_model))



        if self.attn_type == 'bi':

            # beg, end = klen - 1, -qlen

            beg, end = klen, -qlen

        elif self.attn_type == 'uni':

            # beg, end = klen - 1, -1

            beg, end = klen, -1

        else:

            raise ValueError('Unknown `attn_type` {}.'.format(self.attn_type))



        if self.bi_data:

            fwd_pos_seq = torch.arange(beg, end, -1.0, dtype=torch.float)

            bwd_pos_seq = torch.arange(-beg, -end, 1.0, dtype=torch.float)



            if self.clamp_len > 0:

                fwd_pos_seq = fwd_pos_seq.clamp(-self.clamp_len, self.clamp_len)

                bwd_pos_seq = bwd_pos_seq.clamp(-self.clamp_len, self.clamp_len)



            if bsz is not None:

                fwd_pos_emb = self.positional_embedding(fwd_pos_seq, inv_freq, bsz//2)

                bwd_pos_emb = self.positional_embedding(bwd_pos_seq, inv_freq, bsz//2)

            else:

                fwd_pos_emb = self.positional_embedding(fwd_pos_seq, inv_freq)

                bwd_pos_emb = self.positional_embedding(bwd_pos_seq, inv_freq)



            pos_emb = torch.cat([fwd_pos_emb, bwd_pos_emb], dim=1)

        else:

            fwd_pos_seq = torch.arange(beg, end, -1.0)

            if self.clamp_len > 0:

                fwd_pos_seq = fwd_pos_seq.clamp(-self.clamp_len, self.clamp_len)

            pos_emb = self.positional_embedding(fwd_pos_seq, inv_freq, bsz)



        pos_emb = pos_emb.to(next(self.parameters()))

        return pos_emb



    def forward(self, input_ids=None, attention_mask=None, mems=None, perm_mask=None, target_mapping=None,

                token_type_ids=None, input_mask=None, head_mask=None, inputs_embeds=None):

        # the original code for XLNet uses shapes [len, bsz] with the batch dimension at the end

        # but we want a unified interface in the library with the batch size on the first dimension

        # so we move here the first dimension (batch) to the end

        if input_ids is not None and inputs_embeds is not None:

            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")

        elif input_ids is not None:

            input_ids = input_ids.transpose(0, 1).contiguous()

            qlen, bsz = input_ids.shape[0], input_ids.shape[1]

        elif inputs_embeds is not None:

            inputs_embeds.transpose(0, 1).contiguous()

            qlen, bsz = inputs_embeds.shape[0], inputs_embeds.shape[1]

        else:

            raise ValueError("You have to specify either input_ids or inputs_embeds")



        token_type_ids = token_type_ids.transpose(0, 1).contiguous() if token_type_ids is not None else None

        input_mask = input_mask.transpose(0, 1).contiguous() if input_mask is not None else None

        attention_mask = attention_mask.transpose(0, 1).contiguous() if attention_mask is not None else None

        perm_mask = perm_mask.permute(1, 2, 0).contiguous() if perm_mask is not None else None

        target_mapping = target_mapping.permute(1, 2, 0).contiguous() if target_mapping is not None else None





        mlen = mems[0].shape[0] if mems is not None and mems[0] is not None else 0

        klen = mlen + qlen



        dtype_float = next(self.parameters()).dtype

        device = next(self.parameters()).device



        ##### Attention mask

        # causal attention mask

        if self.attn_type == 'uni':

            attn_mask = self.create_mask(qlen, mlen)

            attn_mask = attn_mask[:, :, None, None]

        elif self.attn_type == 'bi':

            attn_mask = None

        else:

            raise ValueError('Unsupported attention type: {}'.format(self.attn_type))



        # data mask: input mask & perm mask

        assert input_mask is None or attention_mask is None, "You can only use one of input_mask (uses 1 for padding) "

        "or attention_mask (uses 0 for padding, added for compatbility with BERT). Please choose one."

        if input_mask is None and attention_mask is not None:

            input_mask = 1.0 - attention_mask

        if input_mask is not None and perm_mask is not None:

            data_mask = input_mask[None] + perm_mask

        elif input_mask is not None and perm_mask is None:

            data_mask = input_mask[None]

        elif input_mask is None and perm_mask is not None:

            data_mask = perm_mask

        else:

            data_mask = None



        if data_mask is not None:

            # all mems can be attended to

            if mlen > 0:

                mems_mask = torch.zeros([data_mask.shape[0], mlen, bsz]).to(data_mask)

                data_mask = torch.cat([mems_mask, data_mask], dim=1)

            if attn_mask is None:

                attn_mask = data_mask[:, :, :, None]

            else:

                attn_mask += data_mask[:, :, :, None]



        if attn_mask is not None:

            attn_mask = (attn_mask > 0).to(dtype_float)



        if attn_mask is not None:

            non_tgt_mask = -torch.eye(qlen).to(attn_mask)

            if mlen > 0:

                non_tgt_mask = torch.cat([torch.zeros([qlen, mlen]).to(attn_mask), non_tgt_mask], dim=-1)

            non_tgt_mask = ((attn_mask + non_tgt_mask[:, :, None, None]) > 0).to(attn_mask)

        else:

            non_tgt_mask = None



        ##### Word embeddings and prepare h & g hidden states

        if inputs_embeds is not None:

            word_emb_k = inputs_embeds

        else:

            word_emb_k = self.word_embedding(input_ids)

        output_h = self.dropout(word_emb_k)

        if target_mapping is not None:

            word_emb_q = self.mask_emb.expand(target_mapping.shape[0], bsz, -1)

        # else:  # We removed the inp_q input which was same as target mapping

        #     inp_q_ext = inp_q[:, :, None]

        #     word_emb_q = inp_q_ext * self.mask_emb + (1 - inp_q_ext) * word_emb_k

            output_g = self.dropout(word_emb_q)

        else:

            output_g = None



        ##### Segment embedding

        if token_type_ids is not None:

            # Convert `token_type_ids` to one-hot `seg_mat`

            if mlen > 0:

                mem_pad = torch.zeros([mlen, bsz], dtype=torch.long, device=device)

                cat_ids = torch.cat([mem_pad, token_type_ids], dim=0)

            else:

                cat_ids = token_type_ids



            # `1` indicates not in the same segment [qlen x klen x bsz]

            seg_mat = (token_type_ids[:, None] != cat_ids[None, :]).long()

            seg_mat = F.one_hot(seg_mat, num_classes=2).to(dtype_float)

        else:

            seg_mat = None



        ##### Positional encoding

        pos_emb = self.relative_positional_encoding(qlen, klen, bsz=bsz)

        pos_emb = self.dropout(pos_emb)



        # Prepare head mask if needed

        # 1.0 in head_mask indicate we keep the head

        # attention_probs has shape bsz x n_heads x N x N

        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] (a head_mask for each layer)

        # and head_mask is converted to shape [num_hidden_layers x qlen x klen x bsz x n_head]

        if head_mask is not None:

            if head_mask.dim() == 1:

                head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(0).unsqueeze(0)

                head_mask = head_mask.expand(self.n_layer, -1, -1, -1, -1)

            elif head_mask.dim() == 2:

                head_mask = head_mask.unsqueeze(1).unsqueeze(1).unsqueeze(1)

            head_mask = head_mask.to(dtype=next(self.parameters()).dtype) # switch to fload if need + fp16 compatibility

        else:

            head_mask = [None] * self.n_layer



        new_mems = ()

        if mems is None:

            mems = [None] * len(self.layer)



        attentions = []

        hidden_states = []

        for i, layer_module in enumerate(self.layer):

            if self.mem_len is not None and self.mem_len > 0 and self.output_past:

                # cache new mems

                new_mems = new_mems + (self.cache_mem(output_h, mems[i]),)

            if self.output_hidden_states:

                hidden_states.append((output_h, output_g) if output_g is not None else output_h)



            outputs = layer_module(output_h, output_g, attn_mask_h=non_tgt_mask, attn_mask_g=attn_mask,

                                   r=pos_emb, seg_mat=seg_mat, mems=mems[i], target_mapping=target_mapping,

                                   head_mask=head_mask[i])

            output_h, output_g = outputs[:2]

            if self.output_attentions:

                attentions.append(outputs[2])



        # Add last hidden state

        if self.output_hidden_states:

            hidden_states.append((output_h, output_g) if output_g is not None else output_h)



        output = self.dropout(output_g if output_g is not None else output_h)



        # Prepare outputs, we transpose back here to shape [bsz, len, hidden_dim] (cf. beginning of forward() method)

        outputs = (output.permute(1, 0, 2).contiguous(),)



        if self.mem_len is not None and self.mem_len > 0 and self.output_past:

            outputs = outputs + (new_mems,)



        if self.output_hidden_states:

            if output_g is not None:

                hidden_states = tuple(h.permute(1, 0, 2).contiguous() for hs in hidden_states for h in hs)

            else:

                hidden_states = tuple(hs.permute(1, 0, 2).contiguous() for hs in hidden_states)

            outputs = outputs + (hidden_states,)

        if self.output_attentions:

            if target_mapping is not None:

                # when target_mapping is provided, there are 2-tuple of attentions

                attentions = tuple(tuple(att_stream.permute(2, 3, 0, 1).contiguous() for att_stream in t) for t in attentions)

            else:

                attentions = tuple(t.permute(2, 3, 0, 1).contiguous() for t in attentions)

            outputs = outputs + (attentions,)



        return outputs  # outputs, (new_mems), (hidden_states), (attentions)





@add_start_docstrings("""XLNet Model with a language modeling head on top

    (linear layer with weights tied to the input embeddings). """,

    XLNET_START_DOCSTRING, XLNET_INPUTS_DOCSTRING)

class XLNetLMHeadModel(XLNetPreTrainedModel):

    r"""

        **labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:

            Labels for language modeling.

            Note that the labels **are shifted** inside the model, i.e. you can set ``lm_labels = input_ids``

            Indices are selected in ``[-1, 0, ..., config.vocab_size]``

            All labels set to ``-1`` are ignored (masked), the loss is only

            computed for labels in ``[0, ..., config.vocab_size]``



    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:

        **loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:

            Language modeling loss.

        **prediction_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, config.vocab_size)``

            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).

        **mems**: (`optional`, returned when ``config.mem_len > 0``)

            list of ``torch.FloatTensor`` (one for each layer):

            that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model

            if config.mem_len > 0 else tuple of None. Can be used to speed up sequential decoding and attend to longer context.

            See details in the docstring of the `mems` input above.

        **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)

            list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)

            of shape ``(batch_size, sequence_length, hidden_size)``:

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.

        **attentions**: (`optional`, returned when ``config.output_attentions=True``)

            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

            When ``target_mapping is not None``, the attentions outputs are a list of 2-tuple of ``torch.FloatTensor``.



    Examples::



        tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased')

        model = XLNetLMHeadModel.from_pretrained('xlnet-large-cased')

        # We show how to setup inputs to predict a next token using a bi-directional context.

        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is very <mask>")).unsqueeze(0)  # We will predict the masked token

        perm_mask = torch.zeros((1, input_ids.shape[1], input_ids.shape[1]), dtype=torch.float)

        perm_mask[:, :, -1] = 1.0  # Previous tokens don't see last token

        target_mapping = torch.zeros((1, 1, input_ids.shape[1]), dtype=torch.float)  # Shape [1, 1, seq_length] => let's predict one token

        target_mapping[0, 0, -1] = 1.0  # Our first (and only) prediction will be the last token of the sequence (the masked token)

        outputs = model(input_ids, perm_mask=perm_mask, target_mapping=target_mapping)

        next_token_logits = outputs[0]  # Output has shape [target_mapping.size(0), target_mapping.size(1), config.vocab_size]



    """

    def __init__(self, config):

        super(XLNetLMHeadModel, self).__init__(config)

        self.attn_type = config.attn_type

        self.same_length = config.same_length



        self.transformer = XLNetModel(config)

        self.lm_loss = nn.Linear(config.d_model, config.n_token, bias=True)



        self.init_weights()



    def get_output_embeddings(self):

        return self.lm_loss



    def forward(self, input_ids=None, attention_mask=None, mems=None, perm_mask=None, target_mapping=None,

                token_type_ids=None, input_mask=None, head_mask=None, inputs_embeds=None, labels=None):

        transformer_outputs = self.transformer(input_ids,

                                               attention_mask=attention_mask,

                                               mems=mems,

                                               perm_mask=perm_mask,

                                               target_mapping=target_mapping,

                                               token_type_ids=token_type_ids,

                                               input_mask=input_mask,

                                               head_mask=head_mask,

                                               inputs_embeds=inputs_embeds)



        logits = self.lm_loss(transformer_outputs[0])



        outputs = (logits,) + transformer_outputs[1:]  # Keep mems, hidden states, attentions if there are in it



        if labels is not None:

            # Flatten the tokens

            loss_fct = CrossEntropyLoss(ignore_index=-1)

            loss = loss_fct(logits.view(-1, logits.size(-1)),

                            labels.view(-1))

            outputs = (loss,) + outputs



        return outputs  # return (loss), logits, (mems), (hidden states), (attentions)





@add_start_docstrings("""XLNet Model with a sequence classification/regression head on top (a linear layer on top of

    the pooled output) e.g. for GLUE tasks. """,

    XLNET_START_DOCSTRING, XLNET_INPUTS_DOCSTRING)

class XLNetForSequenceClassification(XLNetPreTrainedModel):

    r"""

        **labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:

            Labels for computing the sequence classification/regression loss.

            Indices should be in ``[0, ..., config.num_labels - 1]``.

            If ``config.num_labels == 1`` a regression loss is computed (Mean-Square loss),

            If ``config.num_labels > 1`` a classification loss is computed (Cross-Entropy).



    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:

        **loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:

            Classification (or regression if config.num_labels==1) loss.

        **logits**: ``torch.FloatTensor`` of shape ``(batch_size, config.num_labels)``

            Classification (or regression if config.num_labels==1) scores (before SoftMax).

        **mems**: (`optional`, returned when ``config.mem_len > 0``)

            list of ``torch.FloatTensor`` (one for each layer):

            that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model

            if config.mem_len > 0 else tuple of None. Can be used to speed up sequential decoding and attend to longer context.

            See details in the docstring of the `mems` input above.

        **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)

            list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)

            of shape ``(batch_size, sequence_length, hidden_size)``:

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.

        **attentions**: (`optional`, returned when ``config.output_attentions=True``)

            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

            When ``target_mapping is not None``, the attentions outputs are a list of 2-tuple of ``torch.FloatTensor``.



    Examples::



        tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased')

        model = XLNetForSequenceClassification.from_pretrained('xlnet-large-cased')

        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1

        labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1

        outputs = model(input_ids, labels=labels)

        loss, logits = outputs[:2]



    """

    def __init__(self, config):

        super(XLNetForSequenceClassification, self).__init__(config)

        self.num_labels = config.num_labels



        self.transformer = XLNetModel(config)

        self.sequence_summary = SequenceSummary(config)

        self.logits_proj = nn.Linear(config.d_model, config.num_labels)



        self.init_weights()



    def forward(self, input_ids=None, attention_mask=None, mems=None, perm_mask=None, target_mapping=None,

                token_type_ids=None, input_mask=None, head_mask=None, inputs_embeds=None, labels=None):

        transformer_outputs = self.transformer(input_ids,

                                               attention_mask=attention_mask,

                                               mems=mems,

                                               perm_mask=perm_mask,

                                               target_mapping=target_mapping,

                                               token_type_ids=token_type_ids,

                                               input_mask=input_mask,

                                               head_mask=head_mask,

                                               inputs_embeds=inputs_embeds)

        output = transformer_outputs[0]



        output = self.sequence_summary(output)

        logits = self.logits_proj(output)



        outputs = (logits,) + transformer_outputs[1:]  # Keep mems, hidden states, attentions if there are in it



        if labels is not None:

            if self.num_labels == 1:

                #  We are doing regression

                loss_fct = MSELoss()

                loss = loss_fct(logits.view(-1), labels.view(-1))

            else:

                loss_fct = CrossEntropyLoss()

                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))

            outputs = (loss,) + outputs



        return outputs  # return (loss), logits, (mems), (hidden states), (attentions)



@add_start_docstrings("""XLNet Model with a token classification head on top (a linear layer on top of

                      the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """,

                      XLNET_START_DOCSTRING,

                      XLNET_INPUTS_DOCSTRING)

class XLNetForTokenClassification(XLNetPreTrainedModel):

    r"""

    Inputs:

        **input_ids**: ``torch.LongTensor`` of shape ``(batch_size, num_choices, sequence_length)``:

            Indices of input sequence tokens in the vocabulary.

            The second dimension of the input (`num_choices`) indicates the number of choices to scores.

        **token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:

            Segment token indices to indicate first and second portions of the inputs.

            Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``

        **attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:

            Mask to avoid performing attention on padding token indices.

            Mask values selected in ``[0, 1]``:

            ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.

        **head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:

            Mask to nullify selected heads of the self-attention modules.

            Mask values selected in ``[0, 1]``:

            ``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.

        **inputs_embeds**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, embedding_dim)``:

            Optionally, instead of passing ``input_ids`` you can choose to directly pass an embedded representation.

            This is useful if you want more control over how to convert `input_ids` indices into associated vectors

            than the model's internal embedding lookup matrix.

        **labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:

            Labels for computing the multiple choice classification loss.

            Indices should be in ``[0, ..., num_choices]`` where `num_choices` is the size of the second dimension

            of the input tensors. (see `input_ids` above)



    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:

        **loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:

            Classification loss.

        **scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, config.num_labels)``

            Classification scores (before SoftMax).

        **mems**: (`optional`, returned when ``config.mem_len > 0``)

            list of ``torch.FloatTensor`` (one for each layer):

            that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model

            if config.mem_len > 0 else tuple of None. Can be used to speed up sequential decoding and attend to longer context.

            See details in the docstring of the `mems` input above.

        **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)

            list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)

            of shape ``(batch_size, sequence_length, hidden_size)``:

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.

        **attentions**: (`optional`, returned when ``config.output_attentions=True``)

            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.



    Examples::



        tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased')

        model = XLNetForSequenceClassification.from_pretrained('xlnet-large-cased')

        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1

        labels = torch.tensor([1] * input_ids.size(1)).unsqueeze(0)  # Batch size 1

        outputs = model(input_ids, labels=labels)

        scores = outputs[0]



    """

    def __init__(self, config):

        super(XLNetForTokenClassification, self).__init__(config)

        self.num_labels = config.num_labels



        self.transformer = XLNetModel(config)

        self.classifier = nn.Linear(config.hidden_size, config.num_labels)



        self.init_weights()



    def forward(self, input_ids=None, attention_mask=None, mems=None, perm_mask=None, target_mapping=None,

                token_type_ids=None, input_mask=None, head_mask=None, inputs_embeds=None, labels=None):



        outputs = self.transformer(input_ids,

                            attention_mask=attention_mask,

                            mems=mems,

                            perm_mask=perm_mask,

                            target_mapping=target_mapping,

                            token_type_ids=token_type_ids,

                            input_mask=input_mask,

                            head_mask=head_mask,

                            inputs_embeds=inputs_embeds)



        sequence_output = outputs[0]



        logits = self.classifier(sequence_output)



        outputs = (logits,) + outputs[1:]  # Keep mems, hidden states, attentions if there are in it

        if labels is not None:

            loss_fct = CrossEntropyLoss()

            # Only keep active parts of the loss

            if attention_mask is not None:

                active_loss = attention_mask.view(-1) == 1

                active_logits = logits.view(-1, self.num_labels)[active_loss]

                active_labels = labels.view(-1)[active_loss]

                loss = loss_fct(active_logits, active_labels)

            else:

                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))

            outputs = (loss,) + outputs



        return outputs  # return (loss), logits, (mems), (hidden states), (attentions)





@add_start_docstrings("""XLNet Model with a multiple choice classification head on top (a linear layer on top of

    the pooled output and a softmax) e.g. for RACE/SWAG tasks. """,

    XLNET_START_DOCSTRING, XLNET_INPUTS_DOCSTRING)

class XLNetForMultipleChoice(XLNetPreTrainedModel):

    r"""

    Inputs:

        **input_ids**: ``torch.LongTensor`` of shape ``(batch_size, num_choices, sequence_length)``:

            Indices of input sequence tokens in the vocabulary.

            The second dimension of the input (`num_choices`) indicates the number of choices to scores.

        **token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, num_choices, sequence_length)``:

            Segment token indices to indicate first and second portions of the inputs.

            The second dimension of the input (`num_choices`) indicates the number of choices to score.

            Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``

        **attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, num_choices, sequence_length)``:

            Mask to avoid performing attention on padding token indices.

            The second dimension of the input (`num_choices`) indicates the number of choices to score.

            Mask values selected in ``[0, 1]``:

            ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.

        **head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:

            Mask to nullify selected heads of the self-attention modules.

            Mask values selected in ``[0, 1]``:

            ``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.

        **inputs_embeds**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, embedding_dim)``:

            Optionally, instead of passing ``input_ids`` you can choose to directly pass an embedded representation.

            This is useful if you want more control over how to convert `input_ids` indices into associated vectors

            than the model's internal embedding lookup matrix.

        **labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:

            Labels for computing the multiple choice classification loss.

            Indices should be in ``[0, ..., num_choices]`` where `num_choices` is the size of the second dimension

            of the input tensors. (see `input_ids` above)



    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:

        **loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:

            Classification loss.

        **classification_scores**: ``torch.FloatTensor`` of shape ``(batch_size, num_choices)`` where `num_choices` is the size of the second dimension

            of the input tensors. (see `input_ids` above).

            Classification scores (before SoftMax).

        **mems**: (`optional`, returned when ``config.mem_len > 0``)

            list of ``torch.FloatTensor`` (one for each layer):

            that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model

            if config.mem_len > 0 else tuple of None. Can be used to speed up sequential decoding and attend to longer context.

            See details in the docstring of the `mems` input above.

        **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)

            list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)

            of shape ``(batch_size, sequence_length, hidden_size)``:

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.

        **attentions**: (`optional`, returned when ``config.output_attentions=True``)

            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

            When ``target_mapping is not None``, the attentions outputs are a list of 2-tuple of ``torch.FloatTensor``.



    Examples::



        tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased')

        model = XLNetForMultipleChoice.from_pretrained('xlnet-base-cased')

        choices = ["Hello, my dog is cute", "Hello, my cat is amazing"]

        input_ids = torch.tensor([tokenizer.encode(s) for s in choices]).unsqueeze(0)  # Batch size 1, 2 choices

        labels = torch.tensor(1).unsqueeze(0)  # Batch size 1

        outputs = model(input_ids, labels=labels)

        loss, classification_scores = outputs[:2]



    """

    def __init__(self, config):

        super(XLNetForMultipleChoice, self).__init__(config)



        self.transformer = XLNetModel(config)

        self.sequence_summary = SequenceSummary(config)

        self.logits_proj = nn.Linear(config.d_model, 1)



        self.init_weights()



    def forward(self, input_ids=None, token_type_ids=None, input_mask=None, attention_mask=None,

                mems=None, perm_mask=None, target_mapping=None,

                labels=None, head_mask=None, inputs_embeds=None):

        num_choices = input_ids.shape[1]



        flat_input_ids = input_ids.view(-1, input_ids.size(-1))

        flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None

        flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None

        flat_input_mask = input_mask.view(-1, input_mask.size(-1)) if input_mask is not None else None



        transformer_outputs = self.transformer(flat_input_ids, token_type_ids=flat_token_type_ids,

                                               input_mask=flat_input_mask, attention_mask=flat_attention_mask,

                                               mems=mems, perm_mask=perm_mask, target_mapping=target_mapping,

                                               head_mask=head_mask, inputs_embeds=inputs_embeds)





        output = transformer_outputs[0]



        output = self.sequence_summary(output)

        logits = self.logits_proj(output)

        reshaped_logits = logits.view(-1, num_choices)

        outputs = (reshaped_logits,) + transformer_outputs[1:]  # Keep mems, hidden states, attentions if there are in it



        if labels is not None:

            loss_fct = CrossEntropyLoss()

            loss = loss_fct(reshaped_logits, labels.view(-1))

            outputs = (loss,) + outputs



        return outputs  # return (loss), logits, (mems), (hidden states), (attentions)





@add_start_docstrings("""XLNet Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of

    the hidden-states output to compute `span start logits` and `span end logits`). """,

    XLNET_START_DOCSTRING, XLNET_INPUTS_DOCSTRING)

class XLNetForQuestionAnsweringSimple(XLNetPreTrainedModel):

    r"""

        **start_positions**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:

            Labels for position (index) of the start of the labelled span for computing the token classification loss.

            Positions are clamped to the length of the sequence (`sequence_length`).

            Position outside of the sequence are not taken into account for computing the loss.

        **end_positions**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:

            Labels for position (index) of the end of the labelled span for computing the token classification loss.

            Positions are clamped to the length of the sequence (`sequence_length`).

            Position outside of the sequence are not taken into account for computing the loss.



    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:

        **loss**: (`optional`, returned if both ``start_positions`` and ``end_positions`` are provided) ``torch.FloatTensor`` of shape ``(1,)``:

            Classification loss as the sum of start token, end token (and is_impossible if provided) classification losses.

        **start_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length,)``

            Span-start scores (before SoftMax).

        **end_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length,)``

            Span-end scores (before SoftMax).

        **mems**: (`optional`, returned when ``config.mem_len > 0``)

            list of ``torch.FloatTensor`` (one for each layer):

            that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model

            if config.mem_len > 0 else tuple of None. Can be used to speed up sequential decoding and attend to longer context.

            See details in the docstring of the `mems` input above.

        **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)

            list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)

            of shape ``(batch_size, sequence_length, hidden_size)``:

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.

        **attentions**: (`optional`, returned when ``config.output_attentions=True``)

            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

            When ``target_mapping is not None``, the attentions outputs are a list of 2-tuple of ``torch.FloatTensor``.



    Examples::



        tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-en-2048')

        model = XLMForQuestionAnswering.from_pretrained('xlnet-large-cased')

        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1

        start_positions = torch.tensor([1])

        end_positions = torch.tensor([3])

        outputs = model(input_ids, start_positions=start_positions, end_positions=end_positions)

        loss, start_scores, end_scores = outputs[:2]



    """

    def __init__(self, config):

        super(XLNetForQuestionAnsweringSimple, self).__init__(config)

        self.num_labels = config.num_labels



        self.transformer = XLNetModel(config)

        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)



        self.init_weights()



    def forward(self, input_ids=None, attention_mask=None, mems=None, perm_mask=None, target_mapping=None,

                token_type_ids=None, input_mask=None, head_mask=None, inputs_embeds=None,

                start_positions=None, end_positions=None):



        outputs = self.transformer(input_ids,

                                    attention_mask=attention_mask,

                                    mems=mems,

                                    perm_mask=perm_mask,

                                    target_mapping=target_mapping,

                                    token_type_ids=token_type_ids,

                                    input_mask=input_mask,

                                    head_mask=head_mask,

                                    inputs_embeds=inputs_embeds)



        sequence_output = outputs[0]



        logits = self.qa_outputs(sequence_output)

        start_logits, end_logits = logits.split(1, dim=-1)

        start_logits = start_logits.squeeze(-1)

        end_logits = end_logits.squeeze(-1)



        outputs = (start_logits, end_logits,) + outputs[2:]

        if start_positions is not None and end_positions is not None:

            # If we are on multi-GPU, split add a dimension

            if len(start_positions.size()) > 1:

                start_positions = start_positions.squeeze(-1)

            if len(end_positions.size()) > 1:

                end_positions = end_positions.squeeze(-1)

            # sometimes the start/end positions are outside our model inputs, we ignore these terms

            ignored_index = start_logits.size(1)

            start_positions.clamp_(0, ignored_index)

            end_positions.clamp_(0, ignored_index)



            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)

            start_loss = loss_fct(start_logits, start_positions)

            end_loss = loss_fct(end_logits, end_positions)

            total_loss = (start_loss + end_loss) / 2

            outputs = (total_loss,) + outputs



        return outputs  # (loss), start_logits, end_logits, (mems), (hidden_states), (attentions)





@add_start_docstrings("""XLNet Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of

    the hidden-states output to compute `span start logits` and `span end logits`). """,

    XLNET_START_DOCSTRING, XLNET_INPUTS_DOCSTRING)

class XLNetForQuestionAnswering(XLNetPreTrainedModel):

    r"""

        **start_positions**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:

            Labels for position (index) of the start of the labelled span for computing the token classification loss.

            Positions are clamped to the length of the sequence (`sequence_length`).

            Position outside of the sequence are not taken into account for computing the loss.

        **end_positions**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:

            Labels for position (index) of the end of the labelled span for computing the token classification loss.

            Positions are clamped to the length of the sequence (`sequence_length`).

            Position outside of the sequence are not taken into account for computing the loss.

        **is_impossible**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:

            Labels whether a question has an answer or no answer (SQuAD 2.0)

        **cls_index**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:

            Labels for position (index) of the classification token to use as input for computing plausibility of the answer.

        **p_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:

            Optional mask of tokens which can't be in answers (e.g. [CLS], [PAD], ...).

            1.0 means token should be masked. 0.0 mean token is not masked.



    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:

        **loss**: (`optional`, returned if both ``start_positions`` and ``end_positions`` are provided) ``torch.FloatTensor`` of shape ``(1,)``:

            Classification loss as the sum of start token, end token (and is_impossible if provided) classification losses.

        **start_top_log_probs**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)

            ``torch.FloatTensor`` of shape ``(batch_size, config.start_n_top)``

            Log probabilities for the top config.start_n_top start token possibilities (beam-search).

        **start_top_index**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)

            ``torch.LongTensor`` of shape ``(batch_size, config.start_n_top)``

            Indices for the top config.start_n_top start token possibilities (beam-search).

        **end_top_log_probs**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)

            ``torch.FloatTensor`` of shape ``(batch_size, config.start_n_top * config.end_n_top)``

            Log probabilities for the top ``config.start_n_top * config.end_n_top`` end token possibilities (beam-search).

        **end_top_index**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)

            ``torch.LongTensor`` of shape ``(batch_size, config.start_n_top * config.end_n_top)``

            Indices for the top ``config.start_n_top * config.end_n_top`` end token possibilities (beam-search).

        **cls_logits**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)

            ``torch.FloatTensor`` of shape ``(batch_size,)``

            Log probabilities for the ``is_impossible`` label of the answers.

        **mems**: (`optional`, returned when ``config.mem_len > 0``)

            list of ``torch.FloatTensor`` (one for each layer):

            that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model

            if config.mem_len > 0 else tuple of None. Can be used to speed up sequential decoding and attend to longer context.

            See details in the docstring of the `mems` input above.

        **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)

            list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)

            of shape ``(batch_size, sequence_length, hidden_size)``:

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.

        **attentions**: (`optional`, returned when ``config.output_attentions=True``)

            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

            When ``target_mapping is not None``, the attentions outputs are a list of 2-tuple of ``torch.FloatTensor``.



    Examples::



        tokenizer =  XLNetTokenizer.from_pretrained('xlnet-large-cased')

        model = XLMForQuestionAnswering.from_pretrained('xlnet-large-cased')

        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1

        start_positions = torch.tensor([1])

        end_positions = torch.tensor([3])

        outputs = model(input_ids, start_positions=start_positions, end_positions=end_positions)

        loss, start_scores, end_scores = outputs[:2]



    """

    def __init__(self, config):

        super(XLNetForQuestionAnswering, self).__init__(config)

        self.start_n_top = config.start_n_top

        self.end_n_top = config.end_n_top



        self.transformer = XLNetModel(config)

        self.start_logits = PoolerStartLogits(config)

        self.end_logits = PoolerEndLogits(config)

        self.answer_class = PoolerAnswerClass(config)



        self.init_weights()



    def forward(self, input_ids=None, attention_mask=None, mems=None, perm_mask=None, target_mapping=None,

                token_type_ids=None, input_mask=None, head_mask=None, inputs_embeds=None,

                start_positions=None, end_positions=None, is_impossible=None, cls_index=None, p_mask=None,):

        transformer_outputs = self.transformer(input_ids,

                                               attention_mask=attention_mask,

                                               mems=mems,

                                               perm_mask=perm_mask,

                                               target_mapping=target_mapping,

                                               token_type_ids=token_type_ids,

                                               input_mask=input_mask,

                                               head_mask=head_mask,

                                               inputs_embeds=inputs_embeds)

        hidden_states = transformer_outputs[0]

        start_logits = self.start_logits(hidden_states, p_mask=p_mask)



        outputs = transformer_outputs[1:]  # Keep mems, hidden states, attentions if there are in it



        if start_positions is not None and end_positions is not None:

            # If we are on multi-GPU, let's remove the dimension added by batch splitting

            for x in (start_positions, end_positions, cls_index, is_impossible):

                if x is not None and x.dim() > 1:

                    x.squeeze_(-1)



            # during training, compute the end logits based on the ground truth of the start position

            end_logits = self.end_logits(hidden_states, start_positions=start_positions, p_mask=p_mask)



            loss_fct = CrossEntropyLoss()

            start_loss = loss_fct(start_logits, start_positions)

            end_loss = loss_fct(end_logits, end_positions)

            total_loss = (start_loss + end_loss) / 2



            if cls_index is not None and is_impossible is not None:

                # Predict answerability from the representation of CLS and START

                cls_logits = self.answer_class(hidden_states, start_positions=start_positions, cls_index=cls_index)

                loss_fct_cls = nn.BCEWithLogitsLoss()

                cls_loss = loss_fct_cls(cls_logits, is_impossible)



                # note(zhiliny): by default multiply the loss by 0.5 so that the scale is comparable to start_loss and end_loss

                total_loss += cls_loss * 0.5



            outputs = (total_loss,) + outputs



        else:

            # during inference, compute the end logits based on beam search

            bsz, slen, hsz = hidden_states.size()

            start_log_probs = F.softmax(start_logits, dim=-1) # shape (bsz, slen)



            start_top_log_probs, start_top_index = torch.topk(start_log_probs, self.start_n_top, dim=-1) # shape (bsz, start_n_top)

            start_top_index_exp = start_top_index.unsqueeze(-1).expand(-1, -1, hsz) # shape (bsz, start_n_top, hsz)

            start_states = torch.gather(hidden_states, -2, start_top_index_exp) # shape (bsz, start_n_top, hsz)

            start_states = start_states.unsqueeze(1).expand(-1, slen, -1, -1) # shape (bsz, slen, start_n_top, hsz)



            hidden_states_expanded = hidden_states.unsqueeze(2).expand_as(start_states) # shape (bsz, slen, start_n_top, hsz)

            p_mask = p_mask.unsqueeze(-1) if p_mask is not None else None

            end_logits = self.end_logits(hidden_states_expanded, start_states=start_states, p_mask=p_mask)

            end_log_probs = F.softmax(end_logits, dim=1) # shape (bsz, slen, start_n_top)



            end_top_log_probs, end_top_index = torch.topk(end_log_probs, self.end_n_top, dim=1) # shape (bsz, end_n_top, start_n_top)

            end_top_log_probs = end_top_log_probs.view(-1, self.start_n_top * self.end_n_top)

            end_top_index = end_top_index.view(-1, self.start_n_top * self.end_n_top)



            start_states = torch.einsum("blh,bl->bh", hidden_states, start_log_probs)  # get the representation of START as weighted sum of hidden states

            cls_logits = self.answer_class(hidden_states, start_states=start_states, cls_index=cls_index)  # Shape (batch size,): one single `cls_logits` for each sample



            outputs = (start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits) + outputs



        # return start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits

        # or (if labels are provided) (total_loss,)

        return outputs

